This disclosure generally relates to information composition, and more specifically, the invention relates to methods and systems for analytical processing of data from diverse databases.
The most popular method of Business Intelligence (BI), OLAP (Online Analytical Processing) has enabled enterprises to report, monitor and analyze their performance in an online environment. Despite benefits, however, the need for actionable information still lingers on. While OLAP provides necessary capability to drill down to identify products that are not selling well from, for example, thousands of products, it does not provide sufficient information to formulate sales improvement plans. To act upon the result of analysis based on historical data, a host of current details, recent changes and other related information that is readily available in Master Data Model (MDM) systems is required. An ability to drill down historical, aggregated data to discover attention-seeking customers, items, stores and vendors, among other entities, and then to reach out to their rich operational characteristics in MDM systems, in the same context of work, opens up a new possibility for business intelligence.
OLAP recognizes measures of business performance as the primary unit of analysis. Performance is interplay among products, customers, campaigns, regions and channels, among other dimensions, entities or performers of business. Performance is expressed in terms of quantitative measures and key performance indicators, such as Net Margin, Gross Margin, Average Margin or even Margin per Pound of item. Measures do not have their existence independent of dimensions. Measures spring to life only when customers buy items in stores located at different places, for example. While performance could only be post-facto measured, performers could be influenced, persuaded, redesigned or changed to perform better, a priori.
The advent of MDM suggests that performers too have their own dimensions. What acts as a context of analysis in the world of OLAP is the focal subject of analysis in MDM. Dimension is a relative concept. In reality, analytical processes do not have fixed termini, limited sessions and bounded structures. There is a need to create a switch between “what is the subject?” and “what is the context?” of analysis. Taking out a dataset from a data warehouse and joining it with current details in an MDM repository is not technically defying. The challenge is to create a boundless structure that permits continual analytical process.
The idea of linking data warehouses to operational data sources is known. Teradata launched Active Data Warehouse for integrating static snap-shots of data to current operational data [see, Imhoff C., “Active Data Warehousing—The Ultimate Fulfillment of the Operational Data Store”, Teradata Magazine, http://www.teradata.com/t/page/115436/index.html]. It is based on the concept of ODS IV (operational data store Type IV) as a special case where information provided to the ODS (operational data store) comes not only from operational systems but also from data warehouses or specific data marts. The information from a data warehouse or data mart is transferred into the ODS only periodically, usually in a scheduled fashion. Small amounts of pre-aggregated or pre-analyzed data flow from a strategic decision support environment into the ODS for use with more tactical applications. In this, users could segregate the aggregated data and report on line items. For example, daily sales amounts could be broken down and corresponding participant invoice amounts could be reported. Similarly, IDC (International Data Corporation, USA) too presented a case in favor of what is called as Operational BI [see “Operational Business Intelligence: A New Collaborative Environment”, IDC June 2006, http://bpm.knowledgestorm.com/ksbpm/search/viewabstract/84539/index.jsp].